# ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning

arXiv: https://arxiv.org/abs/2503.10166 · HTML: https://mlsanity.com/paper/2503.10166/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 31 of its 69 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [FLAVA: A Foundational Language And Vision Alignment Model](https://mlsanity.com/paper/2112.04482/) (2021)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models](https://mlsanity.com/paper/2108.04024/) (2021)
- [Composing Text and Image for Image Retrieval - An Empirical Odyssey](https://mlsanity.com/paper/1812.07119/) (2018)
- [Zero-Shot Composed Image Retrieval with Textual Inversion](https://mlsanity.com/paper/2303.15247/) (2023)
- [Vision-by-Language for Training-Free Compositional Image Retrieval](https://mlsanity.com/paper/2310.09291/) (2023)
- [Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.06585/) (2017)
- [FashionViL: Fashion-Focused Vision-and-Language Representation Learning](https://mlsanity.com/paper/2207.08150/) (2022)
- [Improving Retrieval Augmented Language Model with Self-Reasoning](https://mlsanity.com/paper/2407.19813/) (2024)
- [Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach](https://mlsanity.com/paper/2406.03411/) (2024)
- [Improving Generative Visual Dialog by Answering Diverse Questions](https://mlsanity.com/paper/1909.10470/) (2019)
- [MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2301.12646/) (2023)
- [HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels](https://mlsanity.com/paper/2407.05795/) (2024)
- [Language Guided Local Infiltration for Interactive Image Retrieval](https://mlsanity.com/paper/2304.07747/) (2023)
- [Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval](https://mlsanity.com/paper/2302.05093/) (2023)

## Built on by (most cited first, 12 of 12)

- [PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments](https://mlsanity.com/paper/2603.23231/) (2026)
- [CalibCLIP: Contextual Calibration of Dominant Semantics for Text-Driven Image Retrieval](https://mlsanity.com/paper/2510.05586/) (2025)
- [UniCVR: From Alignment to Reranking for Unified Zero-Shot Composed Visual Retrieval](https://mlsanity.com/paper/2604.20318/) (2026)
- [EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking](https://mlsanity.com/paper/2608.20886/) (2026)
- [SQUARE: Semantic Query-Augmented Fusion and Efficient Batch Reranking for Training-free Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2509.26330/) (2025)
- [X-Aligner: Composed Visual Retrieval without the Bells and Whistles](https://mlsanity.com/paper/2601.16582/) (2026)
- [VERHallu: Evaluating and Mitigating Event Relation Hallucination in Video Large Language Models](https://mlsanity.com/paper/2601.10010/) (2026)
- [FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval](https://mlsanity.com/paper/2604.09114/) (2026)
- [Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval](https://mlsanity.com/paper/2605.17365/) (2026)
- [Diverse-Intent Multi-Turn Fashion Image Retrieval](https://mlsanity.com/paper/2607.20291/) (2026)
- [Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration](https://mlsanity.com/paper/2608.20357/) (2026)
- [Training-Free Pseudo-Fusion for Composed Image Retrieval with Diffusion Models and Multimodal Large Language Models](https://mlsanity.com/paper/2608.23102/) (2026)

## Abstract (arXiv)

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation, requiring the construction of separate systems for each task. This not only increases system complexity and maintenance costs, but also exacerbates challenges stemming from language ambiguity and complex image content, making it difficult for retrieval systems to provide accurate and reliable results. To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. The key insight behind the unification lies in the compositional nature of language, which transforms diverse LGIR tasks into a generalized text-to-image retrieval process, along with the reasoning of LMMs serving as a universal verification to refine the results. To be specific, in the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity using chain-of-thought (CoT) reasoning. In the second and third stages, we then reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally. Experiments conducted on six LGIR datasets demonstrate that ImageScope outperforms competitive baselines. Comprehensive evaluations and ablation studies further confirm the effectiveness of our design.